Combining Multiple Comparisons Similarity plots for statistical tests

Following on from my previous blopig post, Garrett gave the very helpful suggestion of combining Multiple Comparisons Similarity (MCSim) plots to reduce information redundancy. For example, this an MCSim plot from my previous blog post:

This plot shows effect sizes from a statistical test (specifically Tukey HSD) between mean absolute error (MAE) scores for different molecular featurization methods on a benchmark dataset. Red shows that the method on the y-axis has a greater average MAE score than the method on the x-axis; blue shows the inverse. There is redundancy in this plot, as the same information is displayed in both the upper and lower triangles. Instead, we could plot both the effect size and the p-values from a test on the same MCSim.

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Geometric Deep Learning meets Forces & Equilibrium

Introduction

Graphs provide a powerful mathematical framework for modelling complex systems, from molecular structures to social networks. In many physical and geometric problems, nodes represent particles, and edges encode interactions, often acting like springs. This perspective aligns naturally with Geometric Deep Learning, where learning algorithms leverage graph structures to capture spatial and relational patterns.

Understanding energy functions and the forces derived from them is fundamental to modelling such systems. In physics and computational chemistry, harmonic potentials, which penalise deviations from equilibrium positions, are widely used to describe elastic networks, protein structures, and even diffusion processes. The Laplacian matrix plays a key role in these formulations, linking energy minimisation to force computations in a clean and computationally efficient way.

By formalising these interactions using matrix notation, we gain not only a compact representation but also a foundation for more advanced techniques such as Langevin dynamics, normal mode analysis, and graph-based neural networks for physical simulations.

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The Good (and limitations) of using a Local CoPilot with Ollama

Interactive code editors have been around for a while now, and tools like GitHub Copilot have woven their way into most development pipelines, and for good reason. They’re easy to use, exceptionally helpful (at certain tasks), and have undeniably made life as a developer smoother. Recently, I decided to switch away from relying on GitHub Copilot in favour of a local model for a few key reasons. While I don’t use it all the time, it has proven to be a useful option in many situations. In this blog post, I’ll go over why I made the switch, how I set it up, and share a bit about my experience so far.

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Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data

I’m delighted to report our collaboration (Ísak Valsson, Matthew Warren, Aniket Magarkar, Phil Biggin, & Charlotte Deane), on “Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data”, has been published in Nature’s Communications Chemistry (https://doi.org/10.1038/s42004-025-01428-y).


During his MSc dissertation project in the Department of Statistics, University of Oxford, OPIG member Ísak Valsson developed an attention-based GNN to predict protein-ligand binding affinity called “AEV-PLIG”. It featurizes a ligand’s atoms using Atomic Environment Vectors to describe the Protein-Ligand Interactions found in a 3D protein-ligand complex. AEV-PLIG is free and open source (BSD 3-Clause), available from GitHub at https://github.com/oxpig/AEV-PLIG, and forked at https://github.com/bigginlab/AEV-PLIG.

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Estimating the Generalisability of Machine Learning Models in Drug Discovery

Machine learning (ML) has significantly advanced key computational tasks in drug discovery, including virtual screening, binding affinity prediction, protein-ligand structure prediction (co-folding), and docking. However, the extent to which these models generalise beyond their training data is often overestimated due to shortcomings in benchmarking datasets. Existing benchmarks frequently fail to account for similarities between the training and test sets, leading to inflated performance estimates. This issue is particularly pronounced in tasks where models tend to memorise training examples rather than learning generalisable biophysical principles. The figure below demonstrates two examples of model performance decreasing with increased dissimilarity between training and test data, for co-folding (left) and binding affinity prediction (right).

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LLM Coding Tools – An Overview

We’ve come a long way since GitHub Copilot first showed us what AI-assisted coding could look like. These days, there’s a whole ecosystem of LLM coding tools out there, each with their own strengths and approaches. In this blog, I’ll give you a quick overview to help you figure out which one might work best for your workflow.

Level 1: Interactive Code Assistance

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De novo protein padlocks

Binding a desired protein tightly is important for biotechnology. Recent advances in deep learning have allowed the de novo design of (mostly α-helical) binding protein, sidestepping the laborious process of raising antibodies or nanobodies or evolving affibodies, darpins or similar. These deep learning designed binders will bind with okay affinity, but what if the affinity required were much stronger?
<Enter autocatalytic isopeptide bonds>

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Molecule Networks: data visualization using PyVis

Over the past few years I have explored different data visualization strategies with the goal of rapidly communicating information to medicinal chemists. I have recently fallen in love with “molecule networks” as an intuitive and interactive data visualization strategy. This blog gives a brief tutorial on how to start generating your own molecule networks.

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Baby’s First NeurIPS: A Survival Guide for Conference Newbies

There’s something very surreal about stepping into your first major machine learning conference: suddenly, all those GitHub usernames, paper authors, and protagonists of heated twitter spats become real people, the hallways are buzzing with discussions of papers you’ve been meaning to read, and somehow there are 17,000 other people trying to navigate it all alongside you. That was my experience at NeurIPS this year, and despite feeling like a microplankton in an ocean of ML research, I had a grand time. While some of this success was pure luck, much of it came down to excellent advice from the group’s ML conference veterans and lessons learned through trial and error. So, before the details fade into a blur of posters and coffee breaks, here’s my guide to making the most of your first major ML conference.

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Diagnostics on the Cutting Edge, Software in the Stone Age: A Microbiology Story

The need to treat and control infectious diseases has challenged humanity for millennia, driving a series of remarkable advancements in diagnostic tools and techniques. One of the earliest known legal texts, the Code of Hammurabi, references the visual and tactile diagnosis of leprosy. For centuries, the distinct smell of infected wounds was used to identify gangrene, and in Ancient Greece and Rome, the balance of the four humors (blood, phlegm, black bile, and yellow bile) was a central theory in diagnosing infections.

The invention of the compound microscope in 1590 by Hans and Zacharias Janssen, and its refinements by Robert Hooke and Antonie van Leeuwenhoek, marked a turning point as it enabled the direct observation of microorganisms, thereby linking diseases to their microbial origins. Louis Pasteur’s introduction of liquid media aided Joseph Lister in identifying microbes as the source of surgical infections, whilst Robert Koch’s experiments with Bacillus anthracis firmly established the connection between specific microbes and diseases.

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